TY  - JOUR
T1  - Lesion Area Detection Using Source Image Correlation Coefficient for CT Perfusion Imaging
JF  - IEEE Journal of Biomedical and Health Informatics
Y1  - 2013
A1  - Fan Zhu
A1  - Rodríguez, David
A1  - Carpenter, Trevor K.
A1  - Atkinson, Malcolm P.
A1  - Wardlaw, Joanna M.
KW  - CT , Pattern Recognition , Perfusion Source Images , Segmentation
AB  - Computer tomography (CT) perfusion imaging is widely used to calculate brain hemodynamic quantities such as Cerebral Blood Flow (CBF), Cerebral Blood Volume (CBV) and Mean Transit Time (MTT) that aid the diagnosis of acute stroke. Since perfusion source images contain more information than hemodynamic maps, good utilisation of the source images can lead to better understanding than the hemodynamic maps alone. Correlation-coefficient tests are used in our approach to measure the similarity between healthy tissue time-concentration curves and unknown curves. This information is then used to differentiate penumbra and dead tissues from healthy tissues. The goal of the segmentation is to fully utilize information in the perfusion source images. Our method directly identifies suspected abnormal areas from perfusion source images and then delivers a suggested segmentation of healthy, penumbra and dead tissue. This approach is designed to handle CT perfusion images, but it can also be used to detect lesion areas in MR perfusion images.
VL  - 17
IS  - 5
ER  - 

TY  - JOUR
T1  - An open source toolkit for medical imaging de-identification
JF  - European Radiology
Y1  - 2010
A1  - Rodríguez, David
A1  - Carpenter, Trevor K.
A1  - van Hemert, Jano I.
A1  - Wardlaw, Joanna M.
KW  - Anonymisation
KW  - Data Protection Act (DPA)
KW  - De-identification
KW  - Digital Imaging and Communications in Medicine (DICOM)
KW  - Privacy policies
KW  - Pseudonymisation
KW  - Toolkit
AB  - Objective    Medical imaging acquired for clinical purposes can have several legitimate secondary uses in research projects and teaching libraries. No commonly accepted solution for anonymising these images exists because the amount of personal data that should be preserved varies case by case. Our objective is to provide a flexible mechanism for anonymising Digital Imaging and Communications in Medicine (DICOM) data that meets the requirements for deployment in multicentre trials.  Methods    We reviewed our current de-identification practices and defined the relevant use cases to extract the requirements for the de-identification process. We then used these requirements in the design and implementation of the toolkit. Finally, we tested the toolkit taking as a reference those requirements, including a multicentre deployment.  Results    The toolkit successfully anonymised DICOM data from various sources. Furthermore, it was shown that it could forward anonymous data to remote destinations, remove burned-in annotations, and add tracking information to the header. The toolkit also implements the DICOM standard confidentiality mechanism.  Conclusion    A DICOM de-identification toolkit that facilitates the enforcement of privacy policies was developed. It is highly extensible, provides the necessary flexibility to account for different de-identification requirements and has a low adoption barrier for new users.
VL  - 20
UR  - http://www.springerlink.com/content/j20844338623m167/
IS  - 8
ER  -